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import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
# Load model and tokenizer
model_name = "cross-encoder/ms-marco-MiniLM-L-12-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval() # Set the model to evaluation mode
# Function to get relevance score and relevant excerpt while preserving order
def get_relevance_score_and_excerpt(query, paragraph):
if not query.strip() or not paragraph.strip():
return "Please provide both a query and a document paragraph.", ""
# Tokenize the input
inputs = tokenizer(query, paragraph, return_tensors="pt", truncation=True, padding=True, return_attention_mask=True)
with torch.no_grad():
output = model(**inputs, output_attentions=True) # Get attention scores
# Extract logits and calculate relevance score
logit = output.logits.squeeze().item()
relevance_score = torch.sigmoid(torch.tensor(logit)).item()
# Extract attention scores (use the last attention layer)
attention = output.attentions[-1] # Shape: (batch_size, num_heads, seq_len, seq_len)
# Average across attention heads to get token importance
attention_scores = attention.mean(dim=1).squeeze(0) # Shape: (seq_len, seq_len)
# Tokenize query and paragraph separately to get lengths
query_tokens = tokenizer.tokenize(query)
paragraph_tokens = tokenizer.tokenize(paragraph)
# Extract only paragraph-related attention scores
query_length = len(query_tokens) + 2 # +2 for special tokens like [CLS] and [SEP]
para_start_idx = query_length
para_end_idx = len(inputs["input_ids"][0]) - 1 # Ignore final [SEP] token
para_attention_scores = attention_scores[0, para_start_idx:para_end_idx].mean(dim=0)
# Get indices of top-k attended tokens while preserving order
top_k = min(5, len(paragraph_tokens)) # Extract top 5 tokens or fewer if short
top_indices = para_attention_scores.argsort(descending=True)[:top_k].sort().values # Sort to preserve order
# Extract top tokens based on original order
highlighted_tokens = [paragraph_tokens[i] for i in top_indices]
# Reconstruct the excerpt from ordered tokens
excerpt = tokenizer.convert_tokens_to_string(highlighted_tokens)
return round(relevance_score, 4), excerpt
# Define Gradio interface
interface = gr.Interface(
fn=get_relevance_score_and_excerpt,
inputs=[
gr.Textbox(label="Query", placeholder="Enter your search query..."),
gr.Textbox(label="Document Paragraph", placeholder="Enter a paragraph to match...")
],
outputs=[
gr.Textbox(label="Relevance Score"),
gr.Textbox(label="Most Relevant Excerpt")
],
title="Cross-Encoder Relevance Scoring with Ordered Excerpt Extraction",
description="Enter a query and a document paragraph to get a relevance score and a relevant excerpt in original order.",
allow_flagging="never",
live=True
)
if __name__ == "__main__":
interface.launch() |